df <- read.csv("merged-rev.csv", header =TRUE, sep=",")
#df <- df[complete.cases(df), ]
df
df$ln_novelty <- log(df$novelty+1)
df$ln_total <- log(df$total+1)
df$group = factor(df$group)
df
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-4.7373 -0.2143 0.3493 0.8478 1.7667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.1471 0.1186 43.387 < 2e-16 ***
factor(group)0 -1.0447 0.1653 -6.319 4.99e-10 ***
factor(group)1 -0.4098 0.1634 -2.509 0.012372 *
factor(group)2 -0.6020 0.1624 -3.706 0.000229 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.438 on 631 degrees of freedom
Multiple R-squared: 0.06168, Adjusted R-squared: 0.05722
F-statistic: 13.83 on 3 and 631 DF, p-value: 9.65e-09
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.72895 -0.10146 0.05141 0.14364 0.30273
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.429485 0.035223 12.193 < 2e-16 ***
factor(group)0 -0.121106 0.024055 -5.035 6.31e-07 ***
factor(group)1 -0.122149 0.023729 -5.148 3.56e-07 ***
factor(group)2 -0.058217 0.023417 -2.486 0.01318 *
Q7_Q7_1 -0.021111 0.006899 -3.060 0.00231 **
Q7_Q7_2 0.028960 0.007017 4.127 4.18e-05 ***
Q8_Q8_1 0.006828 0.007283 0.937 0.34889
Q10 0.006321 0.010657 0.593 0.55327
count 0.013205 0.002806 4.706 3.12e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.205 on 610 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.127, Adjusted R-squared: 0.1156
F-statistic: 11.1 on 8 and 610 DF, p-value: 1.067e-14
df$group <- relevel(df$group, ref = "3")
mod1 <- lm(ln_novelty ~ factor(group) + factor(phase) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod1)
Call:
lm(formula = ln_novelty ~ factor(group) + factor(phase) + Q7_Q7_1 +
Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.74856 -0.09893 0.05357 0.14560 0.31947
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412018 0.038061 10.825 < 2e-16 ***
factor(group)0 -0.120733 0.024045 -5.021 6.76e-07 ***
factor(group)1 -0.121842 0.023719 -5.137 3.77e-07 ***
factor(group)2 -0.057849 0.023407 -2.471 0.01373 *
factor(phase)2 0.002902 0.023359 0.124 0.90117
factor(phase)3 0.027766 0.023329 1.190 0.23444
factor(phase)4 0.036093 0.023275 1.551 0.12149
Q7_Q7_1 -0.021078 0.006896 -3.057 0.00234 **
Q7_Q7_2 0.028966 0.007014 4.130 4.13e-05 ***
Q8_Q8_1 0.006883 0.007280 0.945 0.34480
Q10 0.006380 0.010652 0.599 0.54942
count 0.013216 0.002808 4.706 3.13e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2049 on 607 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1321, Adjusted R-squared: 0.1164
F-statistic: 8.402 on 11 and 607 DF, p-value: 7.504e-14
anova(mod, mod1)
Analysis of Variance Table
Model 1: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Model 2: ln_novelty ~ factor(group) + factor(phase) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 610 25.63
2 607 25.48 3 0.1499 1.1903 0.3126
library(lmerTest)
fit.lmer <- lmer(ln_novelty ~ factor(group) + ( 1 | phase), data = df, REML= FALSE)
boundary (singular) fit: see help('isSingular')
fit.lmer
Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
Formula: ln_novelty ~ factor(group) + (1 | phase)
Data: df
AIC BIC logLik deviance df.resid
-149.4723 -122.7506 80.7362 -161.4723 629
Random effects:
Groups Name Std.Dev.
phase (Intercept) 0.0000
Residual 0.2131
Number of obs: 635, groups: phase, 4
Fixed Effects:
(Intercept) factor(group)0 factor(group)1 factor(group)2
0.53572 -0.13948 -0.13047 -0.05857
optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warnings
tapply(df$ln_novelty, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.3307 0.4855 0.5596 0.5357 0.6162 0.6894
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.5206 0.3962 0.6073 0.6858
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.1777 0.5062 0.4053 0.6182 0.6931
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.3871 0.5465 0.4771 0.6084 0.6904
tapply(df$ln_total, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.331 4.764 5.092 5.147 5.520 5.891
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.991 4.830 4.102 5.337 5.869
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.553 5.089 4.737 5.580 5.882
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.615 4.925 4.545 5.450 5.884
library(vtree)
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
vtree version 5.6.5 -- For more information, type: vignette("vtree")
vtree(df, "group")
vtree(df, c("phase", "group"),
fillcolor = c( phase = "#e7d4e8", group = "#99d8c9"),
horiz = FALSE)
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 +
Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-4.6297 -0.2336 0.3334 0.7804 1.9701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.84326 0.23167 20.906 < 2e-16 ***
factor(group)0 -0.98796 0.15821 -6.244 7.97e-10 ***
factor(group)1 -0.42687 0.15607 -2.735 0.006418 **
factor(group)2 -0.60248 0.15402 -3.912 0.000102 ***
Q7_Q7_1 -0.19595 0.04537 -4.319 1.83e-05 ***
Q7_Q7_2 0.19658 0.04615 4.260 2.37e-05 ***
Q8_Q8_1 -0.10785 0.04790 -2.251 0.024713 *
Q10 0.17939 0.07009 2.559 0.010723 *
count 0.12735 0.01845 6.901 1.29e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.348 on 610 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.177, Adjusted R-squared: 0.1662
F-statistic: 16.4 on 8 and 610 DF, p-value: < 2.2e-16
with(df, interaction.plot(group, phase, ln_total, ylim=c(0, max(ln_total)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

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